4 citations · 9 across the 6 of their papers we have counts for
6 papers
Understanding the Resource Cost of Fully Homomorphic Encryption in Quantum Federated Learning
Lukas Böhm, Arjhun Swaminathan, Anika Hannemann +1
Quantum Federated Learning (QFL) enables distributed training of Quantum Machine Learning (QML) models by sharing model gradients instead of raw data. However, these gradients can…
Private, Efficient and Scalable Kernel Learning for Medical Image Analysis
Anika Hannemann, Arjhun Swaminathan, Ali Burak Ünal +1
Medical imaging is key in modern medicine. From magnetic resonance imaging (MRI) to microscopic imaging for blood cell detection, diagnostic medical imaging reveals vital insights…
PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies
Arjhun Swaminathan, Anika Hannemann, Ali Burak Ünal +2
Genome-wide association studies are pivotal in understanding the genetic underpinnings of complex traits and diseases. Collaborative, multi-site GWAS aim to enhance statistical pow…
Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offs
Anika Hannemann, Jan Ewald, Leo Seeger +1
Machine learning on large-scale genomic or transcriptomic data is important for many novel health applications. For example, precision medicine tailors medical treatments to patien…
Is Homomorphic Encryption Feasible for Smart Mobility?
Anika Hannemann, Erik Buchmann
Smart mobility is a promising approach to meet urban transport needs in an environmentally and and user-friendly way. Smart mobility computes itineraries with multiple means of tra…
A Privacy-Preserving Federated Learning Approach for Kernel methods
Anika Hannemann, Ali Burak Ünal, Arjhun Swaminathan +2
It is challenging to implement Kernel methods, if the data sources are distributed and cannot be joined at a trusted third party for privacy reasons. It is even more challenging, i…